_base_ = [
    # '../_base_/models/fpn_r50.py',
    '../_base_/datasets/potsdam.py',
    '../_base_/default_runtime.py',
    '../_base_/schedules/schedule_80k.py'
]
# model settings
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
    type='EncoderDecoder',
    pretrained='open-mmlab://resnest101',
    backbone=dict(
        type='ResNeSt',
        depth=101,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        dilations=(1, 1, 2, 4),
        strides=(1, 2, 1, 1),
        norm_cfg=dict(type='SyncBN', requires_grad=True),
        norm_eval=False,
        style='pytorch',
        contract_dilation=True,
        stem_channels=128,
        radix=2,
        reduction_factor=4,
        avg_down_stride=True),
    neck=dict(
        type='FPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        num_outs=4),
    decode_head=dict(
        type='FPNHead',
        in_channels=[256, 256, 256, 256],
        in_index=[0, 1, 2, 3],
        feature_strides=[4, 8, 16, 32],
        channels=128,
        dropout_ratio=0.1,
        num_classes=6,
        norm_cfg=norm_cfg,
        align_corners=False,
        loss_decode=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
    auxiliary_head=dict(
        type='FCNHead',
        in_channels=256,
        in_index=2,
        channels=256,
        num_convs=1,
        concat_input=False,
        dropout_ratio=0.1,
        num_classes=6,
        norm_cfg=dict(type='SyncBN', requires_grad=True),
        align_corners=False,
        loss_decode=dict(
            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)),
    train_cfg=dict(),
    test_cfg=dict(mode='whole'))
    # model training and testing settings


# optimizer = dict(_delete_=True, type='AdamW', lr=0.0002, weight_decay=0.0001)
optimizer = dict(_delete_=True, type='AdamW', lr=0.0001, weight_decay=0.0001)
optimizer_config = dict()
# learning policy
lr_config = dict(policy='poly', power=0.9, min_lr=0.0, by_epoch=False)




